CRITICAL FIX: Switch from ByteLevel to Whitespace pre-tokenizer — fixes 42% UNK rate on domain token sequences

Update package to v0.4.0 with fine-tuning exports

Update training init with finetune exports

Add finetune.py — finetune_domain_model (HF Trainer Pattern A, auto tabular_features passthrough)

Phase 2D: Fine-tuning pipeline — DomainFinetuneDataset, finetune_domain_model, 139 total tests passing

Update package to v0.3.0 with training exports

Add pretrain.py — pretrain_domain_model with HF Trainer, cosine schedule, DataCollatorForLanguageModeling

Add data_pipeline.py — tokenize_user_sequences, pack_sequences, prepare_clm_dataset

Phase 2C: Pre-training pipeline — data pipeline, sequence packing, HF Trainer CLM, 124 total tests passing

Update package init to v0.2.0 with model exports

Add DCNv2 + JointFusionModel (nuFormer-style Transformer + tabular fusion)

Add PLR embeddings (Gorishniy et al. 2022)

Add DomainTransformerForCausalLM — GPT-style NoPE model with SDPA attention, weight tying, HF Trainer compatible

Add DomainTransformerConfig with presets (24M/85M/330M)

Phase 2B: Model architecture — DomainTransformerForCausalLM (NoPE, GPT-style), PLR embeddings, DCNv2 + JointFusion, 105 passing tests

Add predefined schemas (FINANCE, ECOMMERCE, HEALTHCARE)

Add domain_tokenizer.py — DomainTokenizerBuilder (core assembler, HF integration)

Add field_tokenizers.py — Sign, MagnitudeBucket, Calendar, Categorical, DiscreteNumerical tokenizers

Add tokenizers package init

Add schemas package init

Add schema.py — DomainSchema, FieldSpec, FieldType definitions

Phase 2A: Core tokenizer library — schema, field tokenizers, composite builder, predefined schemas, 72 passing tests